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leading research in the development of cryo-EM methods to solve problems in an important class of challenging materials. This Level A Research Fellow position will contribute to the ARC Discovery Project
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of Physics and Astronomy. This position offers an exceptional opportunity to conduct high quality research in the development and application of new transmission electron microscopy methods (STEM and/or TEM
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. This position offers an exceptional opportunity to conduct high quality research in the development of new transmission electron microscopy methods (STEM and/or TEM) and their application to solve important
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) within the School of Public Health and Preventive Medicine (SPHPM) Monash University. As a major clinical research methods centre for Intensive Care across Australia and New Zealand, the centre delivers
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or qualitative and mixed-methods research related to the project. We are seeking someone with a doctoral qualification in a relevant discipline or equivalent experience in multidisciplinary research
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continuum, from postgraduate research students through to senior researchers. The Pro Vice-Chancellor (Researcher Development) (PVC) is the academic leader for Research Masters and PhD programs, providing
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postgraduate qualification in Data Science / Computer Science (PhD preferred) Strong expertise in Python and/or R, SQL, data engineering and machine learning Experience with EMR systems (Cerner highly desirable
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research, especially in neurorehabilitation or neurological conditions. Briefly describe a project you have led in which you used statistical analysis, qualitative methods or conducted a systematic review
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research tasks relevant to this role (e.g. data collection, data cleaning, evidence synthesis, analysis, or research reporting). Please name the methods or tools you have used. Provide an example of how you
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly